用30组实验数据，让模型自己推理化学分子溶解度，更准更快。
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data
- 把机器学习模型和大模型结合，让小数据也能智能推理。
- 新方法仅4次需要重思，预测偏差超100%的分子少至4个。
- 适合需要快速准确预测分子性质的化工研发人员。
在化学工程领域，传统数据处理与预测方法面临严峻挑战，机器学习和大语言模型（LLMs）也各有局限。本文从30组实验数据出发，探索链式思维（CoT）推理模型在该领域的应用。提出一种分层架构，融合高斯过程、随机森林等代理模型与深度求解器R1:14b及Qwen2:7b等强大LLM。研究两种CoT构建方法：大模型链式思维（LLM-CoT）与机器学习-大模型链式思维（ML-LLM-CoT）。结果显示，构建阶段中，ML-LLM-CoT仅需2处重思，共4次重思；而LLM-CoT需5处重思，总计34次。在预测20个结构差异较大的分子溶解度时，高斯模型、LLM-CoT和ML-LLM-CoT中预测偏差超过100%的分子数分别为7、6、4个。结果表明，ML-LLM-CoT在控制高偏差分子数量、优化平均偏差和提高溶解度判断成功率方面表现更优，为化学工程与分子性质预测提供了更可靠的新方案。该研究突破了传统方法限制，为快速属性预测与工艺优化提供新路径。
原文摘要 · Abstract (English)
In the field of chemical engineering, traditional data-processing and prediction methods face significant challenges. Machine-learning and large-language models (LLMs) also have their respective limitations. This paper explores the application of the Chain-of-Thought (CoT) reasoning model in chemical engineering, starting from 30 experimental data points. By integrating traditional surrogate models like Gaussian processes and random forests with powerful LLMs such as DeepSeek-R1, a hierarchical architecture is proposed. Two CoT-building methods, Large Language Model-Chain of Thought (LLM-CoT) and Machine Learning-Large Language Model-Chain of Thought (ML-LLM-CoT), are studied. The LLM-CoT combines local models DeepSeek-r1:14b and Qwen2:7b with Ollama. The ML-LLM-CoT integrates a pre-trained Gaussian ML model with the LLM-based CoT framework. Our results show that during construction, ML-LLM-CoT is more efficient. It only has 2 points that require rethink and a total of 4 rethink times, while LLM-CoT has 5 points that need to be re-thought and 34 total rethink times. In predicting the solubility of 20 molecules with dissimilar structures, the number of molecules with a prediction deviation higher than 100\% for the Gaussian model, LLM-CoT, and ML-LLM-CoT is 7, 6, and 4 respectively. These results indicate that ML-LLM-CoT performs better in controlling the number of high-deviation molecules, optimizing the average deviation, and achieving a higher success rate in solubility judgment, providing a more reliable method for chemical engineering and molecular property prediction. This study breaks through the limitations of traditional methods and offers new solutions for rapid property prediction and process optimization in chemical engineering.
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